def test_compare_jidt_open_cl_estimator():
"""Compare results from OpenCl and JIDT estimators for AIS calculation."""
dat = Data()
dat.generate_mute_data(100, 2)
max_lag = 5
analysis_opts = {
'cmi_calc_name': 'opencl_kraskov',
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [2, 3]
network_analysis = Single_process_storage(max_lag, analysis_opts, tau=1)
res_opencl = network_analysis.analyse_network(dat, processes)
analysis_opts['cmi_calc_name'] = 'jidt_kraskov'
network_analysis = Single_process_storage(max_lag, analysis_opts, tau=1)
res_jidt = network_analysis.analyse_network(dat, processes)
# Note that I require equality up to three digits. Results become more exact for bigger
# data sizes, but this takes too long for a unit test.
np.testing.assert_approx_equal(res_opencl[2]['ais'], res_jidt[2]['ais'], significant=3,
err_msg='AIS results differ between OpenCl and JIDT estimator.')
np.testing.assert_approx_equal(res_opencl[3]['ais'], res_jidt[3]['ais'], significant=3,
err_msg='AIS results differ between OpenCl and JIDT estimator.')
print('AIS for MUTE data proc 2 - opencl: {0} and jidt: {1}'.format(res_opencl[2]['ais'], res_jidt[2]['ais']))
print('AIS for MUTE data proc 3 - opencl: {0} and jidt: {1}'.format(res_opencl[3]['ais'], res_jidt[3]['ais']))
def test_single_source_storage_opencl():
"""Test AIS estimation in MuTE example network."""
dat = Data()
dat.generate_mute_data(1000, 5)
max_lag = 5
analysis_opts = {
'cmi_calc_name': 'opencl_kraskov',
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1, 2, 3]
network_analysis = Single_process_storage(max_lag, analysis_opts, tau=1)
res = network_analysis.analyse_network(dat, processes)
print('AIS for MUTE data proc 1: {0}'.format(res[1]['ais']))
print('AIS for MUTE data proc 2: {0}'.format(res[2]['ais']))
print('AIS for MUTE data proc 3: {0}'.format(res[3]['ais']))
def test_single_source_storage_opencl():
"""Test AIS estimation in MuTE example network."""
dat = Data()
dat.generate_mute_data(1000, 5)
max_lag = 5
analysis_opts = {
'cmi_calc_name': 'opencl_kraskov',
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1, 2, 3]
network_analysis = Single_process_storage(max_lag, analysis_opts, tau=1)
res = network_analysis.analyse_network(dat, processes)
print('AIS for MUTE data proc 1: {0}'.format(res[1]['ais']))
print('AIS for MUTE data proc 2: {0}'.format(res[2]['ais']))
print('AIS for MUTE data proc 3: {0}'.format(res[3]['ais']))
def test_single_source_storage_opencl():
"""Test AIS estimation in MuTE example network."""
dat = Data()
dat.generate_mute_data(1000, 5)
settings = {
'cmi_estimator': 'OpenCLKraskovCMI',
'max_lag': 5,
'tau': 1,
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1, 2, 3]
network_analysis = Single_process_storage()
res = network_analysis.analyse_network(settings, dat, processes)
print('AIS for MUTE data proc 1: {0}'.format(res[1]['ais']))
print('AIS for MUTE data proc 2: {0}'.format(res[2]['ais']))
print('AIS for MUTE data proc 3: {0}'.format(res[3]['ais']))
def test_single_source_storage_gaussian():
n = 1000
proc_1 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
proc_2 = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
# Cast everything to numpy so the idtxl estimator understands it.
dat = Data(np.array([proc_1, proc_2]), dim_order='ps')
max_lag = 5
analysis_opts = {
'cmi_calc_name': 'jidt_kraskov',
'n_perm_mi': 22,
'alpha_mi': 0.05,
'tail_mi': 'one',
}
processes = [1]
network_analysis = Single_process_storage(max_lag, analysis_opts, tau=1)
res = network_analysis.analyse_network(dat, processes)
print('AIS for random normal data without memory (expected is NaN): {0}'.format(res[1]['ais']))
assert res[1]['ais'] is np.nan, 'Estimator did not return nan for memoryless data.'
